HSTGNN jointly models spatial graph structure and temporal dynamics across pressure, flow, and temperature variables to produce accurate virtual measurements in district heating networks.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.
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Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
HSTGNN jointly models spatial graph structure and temporal dynamics across pressure, flow, and temperature variables to produce accurate virtual measurements in district heating networks.
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Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance
A scalable framework harmonizes spatial and temporal representations via low-rank spatial compression and extended temporal horizons to reduce prediction uncertainty in large-scale spatiotemporal tasks.